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Use of ANFIS Control Approach for SSSC based Damping Controllers Applied in a Two-area Power System

机译:ANFIS控制方法在两区域电力系统中基于SSSC的阻尼控制器的使用

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In an interconnected power system, low frequency electromechanical oscillations are initiated by normal small changes in system loads, and they become much worse following a large disturbance. Flexible AC Transmission System (FACTS) devices are widely recognized as powerful controllers for damping power system oscillations. The standard FACTS controllers are linear controllers which may not guarantee acceptable performance or stability in the event of a major disturbance. To overcome the drawbacks of conventional controllers, ANFIS (Adaptive Neuro-Fuzzy Inference System) control scheme has been developed in this paper, and it has been applied for the external coordinated control of series connected FACTS controllers known as Static Synchronous Series Compensators (SSSCs) employed in a two-area power system. In neuro-fuzzy control method, the simplicity of fuzzy systems and the ability of training in neural networks have been combined. The training data set the parameters of membership functions in fuzzy controller. This ANFIS can track the given input-output data in order to conform to the desired controller. Simulation studies carried out in MATLAB/SIMULINK environment demonstrate that the proposed ANFIS based SSSC controller shows the improved damping performance as compared to conventional SSSC based damping controllers under different operating conditions.
机译:在互连的电源系统中,低频机电振荡是由系统负载的正常微小变化引发的,在大干扰之后它们会变得更糟。柔性交流输电系统(FACTS)设备被公认为是抑制电力系统振荡的强大控制器。标准FACTS控制器是线性控制器,在发生重大干扰时可能无法保证可接受的性能或稳定性。为了克服传统控制器的缺点,本文开发了ANFIS(自适应神经模糊推理系统)控制方案,并将其应用于称为静态同步串联补偿器(SSSC)的串联FACTS控制器的外部协调控制。在两区域电力系统中使用。在神经模糊控制方法中,模糊系统的简单性与神经网络的训练能力相结合。训练数据设置模糊控制器中隶属函数的参数。该ANFIS可以跟踪给定的输入输出数据,以符合所需的控制器。在MATLAB / SIMULINK环境中进行的仿真研究表明,与传统的基于SSSC的阻尼控制器相比,在不同的操作条件下,所提出的基于ANFIS的SSSC控制器具有更高的阻尼性能。

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